Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations17784
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory160.0 B

Variable types

DateTime3
Text5
Categorical7
Numeric5

Alerts

system has constant value "http://snomed.info/sct" Constant
age_category is highly overall correlated with age_of_patient and 1 other fieldsHigh correlation
age_of_patient is highly overall correlated with age_category and 1 other fieldsHigh correlation
income is highly overall correlated with income_categoryHigh correlation
income_category is highly overall correlated with incomeHigh correlation
marital is highly overall correlated with age_category and 1 other fieldsHigh correlation
length_of_procedure_in_hours is highly skewed (γ1 = 50.36332377) Skewed

Reproduction

Analysis started2024-12-03 00:13:58.077922
Analysis finished2024-12-03 00:14:09.543788
Duration11.47 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Distinct15176
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
Minimum1962-09-02 13:37:48
Maximum2024-11-04 08:21:17
2024-12-03T11:14:09.631566image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:09.834339image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct16254
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
Minimum1962-09-02 13:52:48
Maximum2024-11-04 08:40:55
2024-12-03T11:14:09.974618image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:10.115318image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct106
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
2024-12-03T11:14:10.342458image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters640224
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30a6452c-4297-a1ac-977a-6a23237c7b46
2nd row30a6452c-4297-a1ac-977a-6a23237c7b46
3rd row30a6452c-4297-a1ac-977a-6a23237c7b46
4th row30a6452c-4297-a1ac-977a-6a23237c7b46
5th row30a6452c-4297-a1ac-977a-6a23237c7b46
ValueCountFrequency (%)
d27273f0-f62d-7d7f-746d-4565f35cf176 1195
 
6.7%
d1622e8b-d26b-ec81-ffcb-ec4bf2af385b 1143
 
6.4%
bad5a231-3709-952a-cf44-f8d6a52cc214 1105
 
6.2%
4f159375-4ee4-36ab-b464-6d38f6ff2dae 946
 
5.3%
f3884e8a-8b36-1e93-66dd-e910dfab2ef5 871
 
4.9%
655baba7-47ed-22ac-2093-1196ebb44928 794
 
4.5%
cb1b46a1-9cb5-1187-ccc5-9fb7b98aa957 499
 
2.8%
14dc5e57-1b84-3305-c042-86c9fc7e4996 487
 
2.7%
6c602779-9775-f512-2724-fa4e0d0788f5 321
 
1.8%
bd2a8021-2868-6dd2-c17f-bfd7c36fe247 313
 
1.8%
Other values (96) 10110
56.8%
2024-12-03T11:14:10.680737image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 71136
 
11.1%
f 41315
 
6.5%
2 40559
 
6.3%
6 39774
 
6.2%
4 38074
 
5.9%
b 37100
 
5.8%
d 36525
 
5.7%
7 36162
 
5.6%
5 35681
 
5.6%
e 35624
 
5.6%
Other values (7) 228274
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 640224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 71136
 
11.1%
f 41315
 
6.5%
2 40559
 
6.3%
6 39774
 
6.2%
4 38074
 
5.9%
b 37100
 
5.8%
d 36525
 
5.7%
7 36162
 
5.6%
5 35681
 
5.6%
e 35624
 
5.6%
Other values (7) 228274
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 640224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 71136
 
11.1%
f 41315
 
6.5%
2 40559
 
6.3%
6 39774
 
6.2%
4 38074
 
5.9%
b 37100
 
5.8%
d 36525
 
5.7%
7 36162
 
5.6%
5 35681
 
5.6%
e 35624
 
5.6%
Other values (7) 228274
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 640224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 71136
 
11.1%
f 41315
 
6.5%
2 40559
 
6.3%
6 39774
 
6.2%
4 38074
 
5.9%
b 37100
 
5.8%
d 36525
 
5.7%
7 36162
 
5.6%
5 35681
 
5.6%
e 35624
 
5.6%
Other values (7) 228274
35.7%
Distinct5269
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
2024-12-03T11:14:10.881571image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters640224
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2777 ?
Unique (%)15.6%

Sample

1st row953c5138-ce17-4084-3432-1ac23f184528
2nd row953c5138-ce17-4084-3432-1ac23f184528
3rd row0b03e41b-06a6-66fa-b972-acc5a83b134a
4th row0b03e41b-06a6-66fa-b972-acc5a83b134a
5th row0b03e41b-06a6-66fa-b972-acc5a83b134a
ValueCountFrequency (%)
24fb9c92-a5ec-0f61-2daf-26506577c979 66
 
0.4%
9055f8ba-4010-33b5-b0b6-9503d9e2f43e 58
 
0.3%
d0a1f365-ea64-cd77-8658-d8ccc2a23c83 35
 
0.2%
d6a83835-7bbf-5aed-ceb1-1039fa5417e3 29
 
0.2%
b971075e-a389-6e13-e80e-d71b03b83ff9 26
 
0.1%
cf1dbb25-a1e9-b1b8-15c4-23433128fcac 26
 
0.1%
631bd5a6-5e05-7bd9-8dd7-d754c5606369 26
 
0.1%
40fb278c-4821-0c7b-08cc-9f79a2c4e0c4 26
 
0.1%
be80101e-9429-be41-eb41-255339860518 23
 
0.1%
569cfc5d-cfcd-80d8-8dea-ae1c476f4d64 22
 
0.1%
Other values (5259) 17447
98.1%
2024-12-03T11:14:11.221177image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 71136
 
11.1%
6 36088
 
5.6%
0 36085
 
5.6%
2 35852
 
5.6%
b 35841
 
5.6%
a 35773
 
5.6%
1 35699
 
5.6%
7 35658
 
5.6%
c 35647
 
5.6%
f 35592
 
5.6%
Other values (7) 246853
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 640224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 71136
 
11.1%
6 36088
 
5.6%
0 36085
 
5.6%
2 35852
 
5.6%
b 35841
 
5.6%
a 35773
 
5.6%
1 35699
 
5.6%
7 35658
 
5.6%
c 35647
 
5.6%
f 35592
 
5.6%
Other values (7) 246853
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 640224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 71136
 
11.1%
6 36088
 
5.6%
0 36085
 
5.6%
2 35852
 
5.6%
b 35841
 
5.6%
a 35773
 
5.6%
1 35699
 
5.6%
7 35658
 
5.6%
c 35647
 
5.6%
f 35592
 
5.6%
Other values (7) 246853
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 640224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 71136
 
11.1%
6 36088
 
5.6%
0 36085
 
5.6%
2 35852
 
5.6%
b 35841
 
5.6%
a 35773
 
5.6%
1 35699
 
5.6%
7 35658
 
5.6%
c 35647
 
5.6%
f 35592
 
5.6%
Other values (7) 246853
38.6%

system
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
http://snomed.info/sct
17784 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters391248
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttp://snomed.info/sct
2nd rowhttp://snomed.info/sct
3rd rowhttp://snomed.info/sct
4th rowhttp://snomed.info/sct
5th rowhttp://snomed.info/sct

Common Values

ValueCountFrequency (%)
http://snomed.info/sct 17784
100.0%

Length

2024-12-03T11:14:11.345249image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:14:11.436545image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
http://snomed.info/sct 17784
100.0%

Most occurring characters

ValueCountFrequency (%)
t 53352
13.6%
/ 53352
13.6%
s 35568
 
9.1%
n 35568
 
9.1%
o 35568
 
9.1%
p 17784
 
4.5%
h 17784
 
4.5%
: 17784
 
4.5%
m 17784
 
4.5%
e 17784
 
4.5%
Other values (5) 88920
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 391248
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 53352
13.6%
/ 53352
13.6%
s 35568
 
9.1%
n 35568
 
9.1%
o 35568
 
9.1%
p 17784
 
4.5%
h 17784
 
4.5%
: 17784
 
4.5%
m 17784
 
4.5%
e 17784
 
4.5%
Other values (5) 88920
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 391248
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 53352
13.6%
/ 53352
13.6%
s 35568
 
9.1%
n 35568
 
9.1%
o 35568
 
9.1%
p 17784
 
4.5%
h 17784
 
4.5%
: 17784
 
4.5%
m 17784
 
4.5%
e 17784
 
4.5%
Other values (5) 88920
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 391248
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 53352
13.6%
/ 53352
13.6%
s 35568
 
9.1%
n 35568
 
9.1%
o 35568
 
9.1%
p 17784
 
4.5%
h 17784
 
4.5%
: 17784
 
4.5%
m 17784
 
4.5%
e 17784
 
4.5%
Other values (5) 88920
22.7%

snomed_code
Real number (ℝ)

Distinct225
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1105263 × 1013
Minimum3802001
Maximum4.56191 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size139.1 KiB
2024-12-03T11:14:11.536194image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum3802001
5-th percentile34043003
Q11.7120701 × 108
median2.6576401 × 108
Q37.10824 × 108
95-th percentile1.269321 × 109
Maximum4.56191 × 1014
Range4.56191 × 1014
Interquartile range (IQR)5.39617 × 108

Descriptive statistics

Standard deviation9.3196526 × 1013
Coefficient of variation (CV)4.4157955
Kurtosis15.591326
Mean2.1105263 × 1013
Median Absolute Deviation (MAD)1.62067 × 108
Skewness4.1924231
Sum3.75336 × 1017
Variance8.6855925 × 1027
MonotonicityNot monotonic
2024-12-03T11:14:11.675069image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171207006 1817
 
10.2%
265764009 1745
 
9.8%
710824005 1093
 
6.1%
430193006 788
 
4.4%
4.282110001 × 1014738
 
4.1%
103697008 683
 
3.8%
34043003 678
 
3.8%
243085009 650
 
3.7%
1260009003 645
 
3.6%
1260010008 645
 
3.6%
Other values (215) 8302
46.7%
ValueCountFrequency (%)
3802001 125
0.7%
5880005 84
0.5%
10383002 9
 
0.1%
11466000 6
 
< 0.1%
14736009 7
 
< 0.1%
14768001 12
 
0.1%
15081005 6
 
< 0.1%
18946005 8
 
< 0.1%
19589009 3
 
< 0.1%
22523008 1
 
< 0.1%
ValueCountFrequency (%)
4.561910001 × 1014130
 
0.7%
4.282110001 × 1014738
4.1%
1.571000087 × 10123
 
< 0.1%
1290459008 6
 
< 0.1%
1290407002 3
 
< 0.1%
1269321004 26
 
0.1%
1263416007 8
 
< 0.1%
1260010008 645
3.6%
1260009003 645
3.6%
1259293006 55
 
0.3%
Distinct225
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
2024-12-03T11:14:11.870424image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length163
Median length93
Mean length44.592949
Min length10

Characters and Unicode

Total characters793041
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)0.3%

Sample

1st rowPlain X-ray of pelvis (procedure)
2nd rowAdmission to orthopedic department (procedure)
3rd rowMedication reconciliation (procedure)
4th rowAssessment of health and social care needs (procedure)
5th rowAssessment of anxiety (procedure)
ValueCountFrequency (%)
procedure 16900
 
17.6%
of 5954
 
6.2%
dental 4496
 
4.7%
and 3337
 
3.5%
assessment 3183
 
3.3%
screening 2817
 
2.9%
care 2817
 
2.9%
using 2154
 
2.2%
depression 1941
 
2.0%
health 1883
 
2.0%
Other values (447) 50512
52.6%
2024-12-03T11:14:12.252473image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 98361
 
12.4%
79474
 
10.0%
r 63091
 
8.0%
n 47675
 
6.0%
a 46942
 
5.9%
o 46924
 
5.9%
s 41822
 
5.3%
i 40953
 
5.2%
t 39463
 
5.0%
c 36733
 
4.6%
Other values (45) 251603
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 793041
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 98361
 
12.4%
79474
 
10.0%
r 63091
 
8.0%
n 47675
 
6.0%
a 46942
 
5.9%
o 46924
 
5.9%
s 41822
 
5.3%
i 40953
 
5.2%
t 39463
 
5.0%
c 36733
 
4.6%
Other values (45) 251603
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 793041
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 98361
 
12.4%
79474
 
10.0%
r 63091
 
8.0%
n 47675
 
6.0%
a 46942
 
5.9%
o 46924
 
5.9%
s 41822
 
5.3%
i 40953
 
5.2%
t 39463
 
5.0%
c 36733
 
4.6%
Other values (45) 251603
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 793041
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 98361
 
12.4%
79474
 
10.0%
r 63091
 
8.0%
n 47675
 
6.0%
a 46942
 
5.9%
o 46924
 
5.9%
s 41822
 
5.3%
i 40953
 
5.2%
t 39463
 
5.0%
c 36733
 
4.6%
Other values (45) 251603
31.7%

base_cost
Real number (ℝ)

Distinct2333
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean879.00884
Minimum0.32
Maximum32811.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size139.1 KiB
2024-12-03T11:14:12.388169image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile431.4
Q1431.4
median431.4
Q3431.4
95-th percentile3260.472
Maximum32811.41
Range32811.09
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1729.116
Coefficient of variation (CV)1.9671202
Kurtosis58.173311
Mean879.00884
Median Absolute Deviation (MAD)0
Skewness6.57852
Sum15632293
Variance2989842.2
MonotonicityNot monotonic
2024-12-03T11:14:12.529090image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
431.4 12848
72.2%
450.32 707
 
4.0%
862.8 397
 
2.2%
215.7 330
 
1.9%
2261.69 256
 
1.4%
1705.48 232
 
1.3%
3451.2 111
 
0.6%
9046.75 99
 
0.6%
1108.27 84
 
0.5%
25.88 70
 
0.4%
Other values (2323) 2650
 
14.9%
ValueCountFrequency (%)
0.32 1
 
< 0.1%
0.44 1
 
< 0.1%
0.9 1
 
< 0.1%
0.92 1
 
< 0.1%
1.15 1
 
< 0.1%
2.09 1
 
< 0.1%
2.25 1
 
< 0.1%
2.39 1
 
< 0.1%
4.96 2
 
< 0.1%
25.88 70
0.4%
ValueCountFrequency (%)
32811.41 1
 
< 0.1%
32592.18 1
 
< 0.1%
29161.5 1
 
< 0.1%
20647.87 2
 
< 0.1%
20484.38 7
< 0.1%
20211.6 4
< 0.1%
19825.4 1
 
< 0.1%
19154.79 1
 
< 0.1%
18823.5 1
 
< 0.1%
18802.78 1
 
< 0.1%
Distinct60
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
2024-12-03T11:14:12.693961image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length17
Median length11
Mean length8.7598403
Min length7

Characters and Unicode

Total characters155785
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowUnknown
2nd row359817006.0
3rd rowUnknown
4th rowUnknown
5th rowUnknown
ValueCountFrequency (%)
unknown 8645
48.6%
66383009.0 2384
 
13.4%
103697008.0 2093
 
11.8%
72892002.0 1696
 
9.5%
431857002.0 1032
 
5.8%
46177005.0 578
 
3.3%
18718003.0 279
 
1.6%
254837009.0 169
 
1.0%
39898005.0 97
 
0.5%
427898007.0 84
 
0.5%
Other values (50) 727
 
4.1%
2024-12-03T11:14:12.954002image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 30030
19.3%
n 25935
16.6%
. 9139
 
5.9%
3 8872
 
5.7%
8 8714
 
5.6%
k 8645
 
5.5%
o 8645
 
5.5%
U 8645
 
5.5%
w 8645
 
5.5%
6 8007
 
5.1%
Other values (6) 30508
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 155785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 30030
19.3%
n 25935
16.6%
. 9139
 
5.9%
3 8872
 
5.7%
8 8714
 
5.6%
k 8645
 
5.5%
o 8645
 
5.5%
U 8645
 
5.5%
w 8645
 
5.5%
6 8007
 
5.1%
Other values (6) 30508
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 155785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 30030
19.3%
n 25935
16.6%
. 9139
 
5.9%
3 8872
 
5.7%
8 8714
 
5.6%
k 8645
 
5.5%
o 8645
 
5.5%
U 8645
 
5.5%
w 8645
 
5.5%
6 8007
 
5.1%
Other values (6) 30508
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 155785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 30030
19.3%
n 25935
16.6%
. 9139
 
5.9%
3 8872
 
5.7%
8 8714
 
5.6%
k 8645
 
5.5%
o 8645
 
5.5%
U 8645
 
5.5%
w 8645
 
5.5%
6 8007
 
5.1%
Other values (6) 30508
19.6%
Distinct60
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
2024-12-03T11:14:13.105053image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length78
Median length70
Mean length19.861673
Min length7

Characters and Unicode

Total characters353220
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowUnknown
2nd rowClosed fracture of hip (disorder)
3rd rowUnknown
4th rowUnknown
5th rowUnknown
ValueCountFrequency (%)
unknown 8645
19.2%
disorder 5262
 
11.7%
gingivitis 2384
 
5.3%
dental 2220
 
4.9%
procedure 2093
 
4.6%
for 2093
 
4.6%
referral 2093
 
4.6%
patient 2093
 
4.6%
care 2093
 
4.6%
disease 1912
 
4.2%
Other values (121) 14150
31.4%
2024-12-03T11:14:13.392587image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 44968
12.7%
r 31467
 
8.9%
e 30660
 
8.7%
27254
 
7.7%
i 26810
 
7.6%
o 22711
 
6.4%
d 20506
 
5.8%
a 18281
 
5.2%
s 14362
 
4.1%
t 12608
 
3.6%
Other values (41) 103593
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 353220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 44968
12.7%
r 31467
 
8.9%
e 30660
 
8.7%
27254
 
7.7%
i 26810
 
7.6%
o 22711
 
6.4%
d 20506
 
5.8%
a 18281
 
5.2%
s 14362
 
4.1%
t 12608
 
3.6%
Other values (41) 103593
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 353220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 44968
12.7%
r 31467
 
8.9%
e 30660
 
8.7%
27254
 
7.7%
i 26810
 
7.6%
o 22711
 
6.4%
d 20506
 
5.8%
a 18281
 
5.2%
s 14362
 
4.1%
t 12608
 
3.6%
Other values (41) 103593
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 353220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 44968
12.7%
r 31467
 
8.9%
e 30660
 
8.7%
27254
 
7.7%
i 26810
 
7.6%
o 22711
 
6.4%
d 20506
 
5.8%
a 18281
 
5.2%
s 14362
 
4.1%
t 12608
 
3.6%
Other values (41) 103593
29.3%

length_of_procedure_in_hours
Real number (ℝ)

Skewed 

Distinct3334
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69234491
Minimum0.036111111
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size139.1 KiB
2024-12-03T11:14:13.526809image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.036111111
5-th percentile0.17111111
Q10.25
median0.39833333
Q30.63055556
95-th percentile3
Maximum134
Range133.96389
Interquartile range (IQR)0.38055556

Descriptive statistics

Standard deviation1.5688371
Coefficient of variation (CV)2.2659762
Kurtosis3805.1
Mean0.69234491
Median Absolute Deviation (MAD)0.14833333
Skewness50.363324
Sum12312.662
Variance2.4612497
MonotonicityNot monotonic
2024-12-03T11:14:13.669386image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25 3203
 
18.0%
0.5 408
 
2.3%
0.08333333333 153
 
0.9%
0.1666666667 95
 
0.5%
1 41
 
0.2%
3.05 25
 
0.1%
2.433333333 24
 
0.1%
3.266666667 22
 
0.1%
3.233333333 22
 
0.1%
2.733333333 21
 
0.1%
Other values (3324) 13770
77.4%
ValueCountFrequency (%)
0.03611111111 1
 
< 0.1%
0.05055555556 1
 
< 0.1%
0.07166666667 1
 
< 0.1%
0.08333333333 153
0.9%
0.08361111111 3
 
< 0.1%
0.08388888889 1
 
< 0.1%
0.08416666667 3
 
< 0.1%
0.08444444444 1
 
< 0.1%
0.085 3
 
< 0.1%
0.08527777778 3
 
< 0.1%
ValueCountFrequency (%)
134 1
 
< 0.1%
98 1
 
< 0.1%
37 1
 
< 0.1%
34 1
 
< 0.1%
30 1
 
< 0.1%
28 1
 
< 0.1%
24 1
 
< 0.1%
5.916666667 1
 
< 0.1%
4.35 1
 
< 0.1%
3.983333333 11
0.1%
Distinct100
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
Minimum1914-03-03 00:00:00
Maximum2023-03-01 00:00:00
2024-12-03T11:14:13.804934image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:14.067266image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

marital
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
M
9369 
Unknown
3502 
D
2670 
S
1720 
W
 
523

Length

Max length7
Median length1
Mean length2.1815115
Min length1

Characters and Unicode

Total characters38796
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 9369
52.7%
Unknown 3502
 
19.7%
D 2670
 
15.0%
S 1720
 
9.7%
W 523
 
2.9%

Length

2024-12-03T11:14:14.206502image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:14:14.327614image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
m 9369
52.7%
unknown 3502
 
19.7%
d 2670
 
15.0%
s 1720
 
9.7%
w 523
 
2.9%

Most occurring characters

ValueCountFrequency (%)
n 10506
27.1%
M 9369
24.1%
U 3502
 
9.0%
k 3502
 
9.0%
o 3502
 
9.0%
w 3502
 
9.0%
D 2670
 
6.9%
S 1720
 
4.4%
W 523
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38796
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 10506
27.1%
M 9369
24.1%
U 3502
 
9.0%
k 3502
 
9.0%
o 3502
 
9.0%
w 3502
 
9.0%
D 2670
 
6.9%
S 1720
 
4.4%
W 523
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38796
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 10506
27.1%
M 9369
24.1%
U 3502
 
9.0%
k 3502
 
9.0%
o 3502
 
9.0%
w 3502
 
9.0%
D 2670
 
6.9%
S 1720
 
4.4%
W 523
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38796
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 10506
27.1%
M 9369
24.1%
U 3502
 
9.0%
k 3502
 
9.0%
o 3502
 
9.0%
w 3502
 
9.0%
D 2670
 
6.9%
S 1720
 
4.4%
W 523
 
1.3%

race
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
white
13551 
asian
2195 
other
 
1169
black
 
791
native
 
78

Length

Max length6
Median length5
Mean length5.004386
Min length5

Characters and Unicode

Total characters88998
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowwhite
3rd rowwhite
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
white 13551
76.2%
asian 2195
 
12.3%
other 1169
 
6.6%
black 791
 
4.4%
native 78
 
0.4%

Length

2024-12-03T11:14:14.450041image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:14:14.556418image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
white 13551
76.2%
asian 2195
 
12.3%
other 1169
 
6.6%
black 791
 
4.4%
native 78
 
0.4%

Most occurring characters

ValueCountFrequency (%)
i 15824
17.8%
e 14798
16.6%
t 14798
16.6%
h 14720
16.5%
w 13551
15.2%
a 5259
 
5.9%
n 2273
 
2.6%
s 2195
 
2.5%
o 1169
 
1.3%
r 1169
 
1.3%
Other values (5) 3242
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 88998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 15824
17.8%
e 14798
16.6%
t 14798
16.6%
h 14720
16.5%
w 13551
15.2%
a 5259
 
5.9%
n 2273
 
2.6%
s 2195
 
2.5%
o 1169
 
1.3%
r 1169
 
1.3%
Other values (5) 3242
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 88998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 15824
17.8%
e 14798
16.6%
t 14798
16.6%
h 14720
16.5%
w 13551
15.2%
a 5259
 
5.9%
n 2273
 
2.6%
s 2195
 
2.5%
o 1169
 
1.3%
r 1169
 
1.3%
Other values (5) 3242
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 88998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 15824
17.8%
e 14798
16.6%
t 14798
16.6%
h 14720
16.5%
w 13551
15.2%
a 5259
 
5.9%
n 2273
 
2.6%
s 2195
 
2.5%
o 1169
 
1.3%
r 1169
 
1.3%
Other values (5) 3242
 
3.6%

ethnicity
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
nonhispanic
15821 
hispanic
1963 

Length

Max length11
Median length11
Mean length10.66886
Min length8

Characters and Unicode

Total characters189735
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonhispanic
2nd rownonhispanic
3rd rownonhispanic
4th rownonhispanic
5th rownonhispanic

Common Values

ValueCountFrequency (%)
nonhispanic 15821
89.0%
hispanic 1963
 
11.0%

Length

2024-12-03T11:14:14.680939image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:14:14.787393image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
nonhispanic 15821
89.0%
hispanic 1963
 
11.0%

Most occurring characters

ValueCountFrequency (%)
n 49426
26.1%
i 35568
18.7%
h 17784
 
9.4%
s 17784
 
9.4%
a 17784
 
9.4%
p 17784
 
9.4%
c 17784
 
9.4%
o 15821
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 189735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 49426
26.1%
i 35568
18.7%
h 17784
 
9.4%
s 17784
 
9.4%
a 17784
 
9.4%
p 17784
 
9.4%
c 17784
 
9.4%
o 15821
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 189735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 49426
26.1%
i 35568
18.7%
h 17784
 
9.4%
s 17784
 
9.4%
a 17784
 
9.4%
p 17784
 
9.4%
c 17784
 
9.4%
o 15821
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 189735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 49426
26.1%
i 35568
18.7%
h 17784
 
9.4%
s 17784
 
9.4%
a 17784
 
9.4%
p 17784
 
9.4%
c 17784
 
9.4%
o 15821
 
8.3%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
F
9437 
M
8347 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17784
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F 9437
53.1%
M 8347
46.9%

Length

2024-12-03T11:14:14.887038image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:14:14.973747image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
f 9437
53.1%
m 8347
46.9%

Most occurring characters

ValueCountFrequency (%)
F 9437
53.1%
M 8347
46.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17784
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 9437
53.1%
M 8347
46.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17784
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 9437
53.1%
M 8347
46.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17784
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 9437
53.1%
M 8347
46.9%

income
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104532.45
Minimum7361
Maximum816851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size139.1 KiB
2024-12-03T11:14:15.092671image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum7361
5-th percentile16969
Q149737
median86384
Q3118047
95-th percentile189277
Maximum816851
Range809490
Interquartile range (IQR)68310

Descriptive statistics

Standard deviation116932.58
Coefficient of variation (CV)1.1186247
Kurtosis22.04659
Mean104532.45
Median Absolute Deviation (MAD)31663
Skewness4.4189992
Sum1.8590051 × 109
Variance1.3673228 × 1010
MonotonicityNot monotonic
2024-12-03T11:14:15.246219image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92537 1323
 
7.4%
82922 1195
 
6.7%
90297 1107
 
6.2%
118047 1105
 
6.2%
189277 946
 
5.3%
35860 871
 
4.9%
63727 499
 
2.8%
72413 487
 
2.7%
95344 396
 
2.2%
179090 321
 
1.8%
Other values (90) 9534
53.6%
ValueCountFrequency (%)
7361 237
1.3%
7873 70
 
0.4%
8615 95
0.5%
8752 162
0.9%
10135 6
 
< 0.1%
10682 89
 
0.5%
12128 131
0.7%
16969 183
1.0%
17382 105
0.6%
18258 119
0.7%
ValueCountFrequency (%)
816851 126
 
0.7%
762068 67
 
0.4%
742063 206
 
1.2%
550030 99
 
0.6%
545255 2
 
< 0.1%
198522 106
 
0.6%
198442 150
 
0.8%
189277 946
5.3%
188023 122
 
0.7%
179090 321
 
1.8%

income_category
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
high-income
8805 
medium-income
4623 
low-income
4356 

Length

Max length13
Median length11
Mean length11.274966
Min length10

Characters and Unicode

Total characters200514
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhigh-income
2nd rowhigh-income
3rd rowhigh-income
4th rowhigh-income
5th rowhigh-income

Common Values

ValueCountFrequency (%)
high-income 8805
49.5%
medium-income 4623
26.0%
low-income 4356
24.5%

Length

2024-12-03T11:14:15.376093image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:14:15.482628image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
high-income 8805
49.5%
medium-income 4623
26.0%
low-income 4356
24.5%

Most occurring characters

ValueCountFrequency (%)
i 31212
15.6%
m 27030
13.5%
e 22407
11.2%
o 22140
11.0%
n 17784
8.9%
- 17784
8.9%
c 17784
8.9%
h 17610
8.8%
g 8805
 
4.4%
d 4623
 
2.3%
Other values (3) 13335
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200514
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 31212
15.6%
m 27030
13.5%
e 22407
11.2%
o 22140
11.0%
n 17784
8.9%
- 17784
8.9%
c 17784
8.9%
h 17610
8.8%
g 8805
 
4.4%
d 4623
 
2.3%
Other values (3) 13335
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200514
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 31212
15.6%
m 27030
13.5%
e 22407
11.2%
o 22140
11.0%
n 17784
8.9%
- 17784
8.9%
c 17784
8.9%
h 17610
8.8%
g 8805
 
4.4%
d 4623
 
2.3%
Other values (3) 13335
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200514
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 31212
15.6%
m 27030
13.5%
e 22407
11.2%
o 22140
11.0%
n 17784
8.9%
- 17784
8.9%
c 17784
8.9%
h 17610
8.8%
g 8805
 
4.4%
d 4623
 
2.3%
Other values (3) 13335
6.7%

age_of_patient
Real number (ℝ)

High correlation 

Distinct4243
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.380178
Minimum0
Maximum110.04384
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size139.1 KiB
2024-12-03T11:14:15.603559image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.049315
Q130.876712
median51.090411
Q365.569863
95-th percentile80.161096
Maximum110.04384
Range110.04384
Interquartile range (IQR)34.693151

Descriptive statistics

Standard deviation22.903156
Coefficient of variation (CV)0.47339958
Kurtosis-0.78133396
Mean48.380178
Median Absolute Deviation (MAD)16.991781
Skewness-0.1300737
Sum860393.08
Variance524.55455
MonotonicityNot monotonic
2024-12-03T11:14:15.734843image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.06575342 109
 
0.6%
10.04931507 100
 
0.6%
12.0630137 100
 
0.6%
14.09589041 90
 
0.5%
15.11232877 88
 
0.5%
13.07945205 88
 
0.5%
8.016438356 79
 
0.4%
16.12876712 73
 
0.4%
19.17808219 71
 
0.4%
59.18356164 69
 
0.4%
Other values (4233) 16917
95.1%
ValueCountFrequency (%)
0 5
< 0.1%
0.09589041096 6
< 0.1%
0.2684931507 5
< 0.1%
0.4410958904 3
 
< 0.1%
0.6904109589 8
< 0.1%
0.8849315068 2
 
< 0.1%
0.898630137 2
 
< 0.1%
0.9095890411 1
 
< 0.1%
0.9397260274 8
< 0.1%
0.9589041096 1
 
< 0.1%
ValueCountFrequency (%)
110.0438356 13
0.1%
110.0054795 8
< 0.1%
108.9890411 4
 
< 0.1%
108.0109589 8
< 0.1%
107.9726027 3
 
< 0.1%
107.709589 2
 
< 0.1%
107.7068493 2
 
< 0.1%
107.7041096 1
 
< 0.1%
107.7013699 1
 
< 0.1%
107.6986301 2
 
< 0.1%

age_category
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.1 KiB
adult
10821 
senior
4797 
children
2166 

Length

Max length8
Median length5
Mean length5.6351215
Min length5

Characters and Unicode

Total characters100215
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadult
2nd rowadult
3rd rowadult
4th rowadult
5th rowadult

Common Values

ValueCountFrequency (%)
adult 10821
60.8%
senior 4797
27.0%
children 2166
 
12.2%

Length

2024-12-03T11:14:15.887774image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:14:16.013441image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
adult 10821
60.8%
senior 4797
27.0%
children 2166
 
12.2%

Most occurring characters

ValueCountFrequency (%)
d 12987
13.0%
l 12987
13.0%
a 10821
10.8%
u 10821
10.8%
t 10821
10.8%
e 6963
6.9%
n 6963
6.9%
i 6963
6.9%
r 6963
6.9%
s 4797
 
4.8%
Other values (3) 9129
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 12987
13.0%
l 12987
13.0%
a 10821
10.8%
u 10821
10.8%
t 10821
10.8%
e 6963
6.9%
n 6963
6.9%
i 6963
6.9%
r 6963
6.9%
s 4797
 
4.8%
Other values (3) 9129
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 12987
13.0%
l 12987
13.0%
a 10821
10.8%
u 10821
10.8%
t 10821
10.8%
e 6963
6.9%
n 6963
6.9%
i 6963
6.9%
r 6963
6.9%
s 4797
 
4.8%
Other values (3) 9129
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 12987
13.0%
l 12987
13.0%
a 10821
10.8%
u 10821
10.8%
t 10821
10.8%
e 6963
6.9%
n 6963
6.9%
i 6963
6.9%
r 6963
6.9%
s 4797
 
4.8%
Other values (3) 9129
9.1%

Interactions

2024-12-03T11:14:07.714615image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:59.487857image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:03.496918image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:04.987371image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:06.385053image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:08.555031image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:00.886865image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:04.488433image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:05.961065image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:07.258052image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:08.649137image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:01.510876image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:04.610760image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:06.065232image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:07.357975image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:08.740805image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:02.182125image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:04.724797image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:06.174658image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:07.472831image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:08.844168image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:02.894280image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:04.872030image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:06.286811image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:14:07.584459image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2024-12-03T11:14:16.102971image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
age_categoryage_of_patientbase_costethnicitygenderincomeincome_categorylength_of_procedure_in_hoursmaritalracesnomed_code
age_category1.0000.8860.0950.2020.1650.2660.1770.0000.5680.3100.442
age_of_patient0.8861.000-0.0190.3140.4000.1770.3350.2120.5280.4410.096
base_cost0.095-0.0191.0000.0750.1430.0890.0310.1500.0540.032-0.195
ethnicity0.2020.3140.0751.0000.1630.2450.0370.0170.1580.1740.255
gender0.1650.4000.1430.1631.0000.3630.1810.0000.2580.4090.353
income0.2660.1770.0890.2450.3631.0000.7070.0750.1720.239-0.000
income_category0.1770.3350.0310.0370.1810.7071.0000.0000.2250.2440.206
length_of_procedure_in_hours0.0000.2120.1500.0170.0000.0750.0001.0000.0050.0000.122
marital0.5680.5280.0540.1580.2580.1720.2250.0051.0000.2600.251
race0.3100.4410.0320.1740.4090.2390.2440.0000.2601.0000.190
snomed_code0.4420.096-0.1950.2550.353-0.0000.2060.1220.2510.1901.000

Missing values

2024-12-03T11:14:09.124243image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-03T11:14:09.406166image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

start_timestop_timepatient_idencounter_idsystemsnomed_codeprocedure_descriptionbase_costreason_code_for_procedurereason_description_for_procedurelength_of_procedure_in_hoursbirthdatemaritalraceethnicitygenderincomeincome_categoryage_of_patientage_category
02015-09-28 09:04:482015-09-28 09:34:4830a6452c-4297-a1ac-977a-6a23237c7b46953c5138-ce17-4084-3432-1ac23f184528http://snomed.info/sct713021002Plain X-ray of pelvis (procedure)431.4UnknownUnknown0.5000001994-02-06MwhitenonhispanicM100511high-income21.654795adult
12015-09-28 09:04:482015-09-28 11:02:4830a6452c-4297-a1ac-977a-6a23237c7b46953c5138-ce17-4084-3432-1ac23f184528http://snomed.info/sct305428000Admission to orthopedic department (procedure)431.4359817006.0Closed fracture of hip (disorder)1.9666671994-02-06MwhitenonhispanicM100511high-income21.654795adult
22016-04-10 09:04:482016-04-10 09:19:4830a6452c-4297-a1ac-977a-6a23237c7b460b03e41b-06a6-66fa-b972-acc5a83b134ahttp://snomed.info/sct430193006Medication reconciliation (procedure)852.4UnknownUnknown0.2500001994-02-06MwhitenonhispanicM100511high-income22.189041adult
32016-04-10 09:04:482016-04-10 10:00:4530a6452c-4297-a1ac-977a-6a23237c7b460b03e41b-06a6-66fa-b972-acc5a83b134ahttp://snomed.info/sct710824005Assessment of health and social care needs (procedure)431.4UnknownUnknown0.9325001994-02-06MwhitenonhispanicM100511high-income22.189041adult
42016-04-10 10:00:452016-04-10 10:22:0330a6452c-4297-a1ac-977a-6a23237c7b460b03e41b-06a6-66fa-b972-acc5a83b134ahttp://snomed.info/sct710841007Assessment of anxiety (procedure)431.4UnknownUnknown0.3550001994-02-06MwhitenonhispanicM100511high-income22.189041adult
52016-04-10 10:22:032016-04-10 10:57:3530a6452c-4297-a1ac-977a-6a23237c7b460b03e41b-06a6-66fa-b972-acc5a83b134ahttp://snomed.info/sct866148006Screening for domestic abuse (procedure)431.4UnknownUnknown0.5922221994-02-06MwhitenonhispanicM100511high-income22.189041adult
62016-04-10 10:57:352016-04-10 11:12:0930a6452c-4297-a1ac-977a-6a23237c7b460b03e41b-06a6-66fa-b972-acc5a83b134ahttp://snomed.info/sct171207006Depression screening (procedure)431.4UnknownUnknown0.2427781994-02-06MwhitenonhispanicM100511high-income22.189041adult
72016-04-10 11:12:092016-04-10 11:33:0230a6452c-4297-a1ac-977a-6a23237c7b460b03e41b-06a6-66fa-b972-acc5a83b134ahttp://snomed.info/sct171207006Depression screening (procedure)431.4UnknownUnknown0.3480561994-02-06MwhitenonhispanicM100511high-income22.189041adult
82016-04-10 11:33:022016-04-10 11:46:1530a6452c-4297-a1ac-977a-6a23237c7b460b03e41b-06a6-66fa-b972-acc5a83b134ahttp://snomed.info/sct428211000124100Assessment of substance use (procedure)431.4UnknownUnknown0.2202781994-02-06MwhitenonhispanicM100511high-income22.189041adult
92016-04-10 11:46:152016-04-10 12:07:2830a6452c-4297-a1ac-977a-6a23237c7b460b03e41b-06a6-66fa-b972-acc5a83b134ahttp://snomed.info/sct713106006Screening for drug abuse (procedure)431.4UnknownUnknown0.3536111994-02-06MwhitenonhispanicM100511high-income22.189041adult
start_timestop_timepatient_idencounter_idsystemsnomed_codeprocedure_descriptionbase_costreason_code_for_procedurereason_description_for_procedurelength_of_procedure_in_hoursbirthdatemaritalraceethnicitygenderincomeincome_categoryage_of_patientage_category
177742024-09-26 09:58:212024-09-26 10:38:50f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc8fb043ba-08be-2a3a-1011-c3019f2b7d07http://snomed.info/sct225362009Dental care (regime/therapy)431.466383009.0Gingivitis (disorder)0.6747221951-11-22SasiannonhispanicF92537high-income72.89589senior
177752024-09-26 10:38:502024-09-26 11:25:53f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc8fb043ba-08be-2a3a-1011-c3019f2b7d07http://snomed.info/sct1260009003Removal of supragingival plaque and calculus from all teeth using dental instrument (procedure)431.466383009.0Gingivitis (disorder)0.7841671951-11-22SasiannonhispanicF92537high-income72.89589senior
177762024-09-26 11:25:532024-09-26 12:16:45f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc8fb043ba-08be-2a3a-1011-c3019f2b7d07http://snomed.info/sct1260010008Removal of subgingival plaque and calculus from all teeth using dental instrument (procedure)431.466383009.0Gingivitis (disorder)0.8477781951-11-22SasiannonhispanicF92537high-income72.89589senior
177772024-09-26 12:16:452024-09-26 12:46:45f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc8fb043ba-08be-2a3a-1011-c3019f2b7d07http://snomed.info/sct241046008Dental plain X-ray bitewing (procedure)431.4UnknownUnknown0.5000001951-11-22SasiannonhispanicF92537high-income72.89589senior
177782024-09-26 12:16:452024-09-26 12:25:12f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc8fb043ba-08be-2a3a-1011-c3019f2b7d07http://snomed.info/sct81733005Dental surgical procedure (procedure)431.4427898007.0Infection of tooth (disorder)0.1408331951-11-22SasiannonhispanicF92537high-income72.89589senior
177792024-09-26 12:25:122024-09-26 13:45:10f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc8fb043ba-08be-2a3a-1011-c3019f2b7d07http://snomed.info/sct3802001Dental application of desensitizing medicament (procedure)431.4427898007.0Infection of tooth (disorder)1.3327781951-11-22SasiannonhispanicF92537high-income72.89589senior
177802024-09-26 13:45:102024-09-26 14:25:44f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc8fb043ba-08be-2a3a-1011-c3019f2b7d07http://snomed.info/sct173291009Simple extraction of tooth (procedure)431.4427898007.0Infection of tooth (disorder)0.6761111951-11-22SasiannonhispanicF92537high-income72.89589senior
177812024-09-26 14:25:442024-09-26 15:34:23f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc8fb043ba-08be-2a3a-1011-c3019f2b7d07http://snomed.info/sct456191000124101Postoperative care for dental procedure (regime/therapy)431.4UnknownUnknown1.1441671951-11-22SasiannonhispanicF92537high-income72.89589senior
177822024-09-26 15:34:232024-09-26 16:19:44f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc8fb043ba-08be-2a3a-1011-c3019f2b7d07http://snomed.info/sct274788003Examination of gingivae (procedure)431.466383009.0Gingivitis (disorder)0.7558331951-11-22SasiannonhispanicF92537high-income72.89589senior
177832024-09-26 16:19:442024-09-26 16:31:42f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc8fb043ba-08be-2a3a-1011-c3019f2b7d07http://snomed.info/sct243085009Oral health education (procedure)431.466383009.0Gingivitis (disorder)0.1994441951-11-22SasiannonhispanicF92537high-income72.89589senior